ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2006.13461v3
• Date: Fri, 7 Aug 2020 01:18:45 GMT
• Title: ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation
• Authors: Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang and Qi Tian
• Abstract summary: We propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
• Score: 99.90263375737362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In medical image analysis, semi-supervised learning is an effective method to extract knowledge from a small amount of labeled data and a large amount of unlabeled data. This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself. To alleviate this issue, we propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings. With slight modification, ATSO transfers well to natural image segmentation for autonomous driving data.

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